CRII: SHF: RUI: Custom Hardware Accelerators for Privacy-Preserving Image Processing

同态加密是一种重要的密码学技术，它允许在不解密的情况下对加密数据进行直接计算。计算结果是完全加密的，只有数据所有者才能使用私钥对其解密。因此，云服务提供商等数据分析师无法从数据中查看任何私人或敏感信息。同态加密的有前途的应用包括金融和医疗数据分析以及保护隐私的机器学习，同态加密还可以用于更多样化的应用，如基因组学、国家安全/关键基础设施和选举。一些新兴的应用使得同态加密变得越来越重要。然而，同态加密的处理速度较慢，这使得它在许多关键应用程序中不切实际。此外，它只支持加密数据的加法和/或乘法，这限制了它的应用范围。该项目将使用自定义硬件加速器和一种近似使用加法和乘法的算术和逻辑运算的数值方法来解决这些问题。该项目将扩大基于同态加密的实际应用范围，并为学生提供在数字系统设计、软件编程和密码学等各个领域获得经验的机会。该项目将重点关注基于同态加密的图像处理应用程序。在典型的基于同态加密的系统中，用户对本地设备上的私有数据进行加密，并将其发送到执行基于同态加密的应用程序的云服务器。然而，在原始图像中包含噪声或亮度失真会对应用结果产生负面影响。因此，该项目的目标之一是开发定制硬件加速器，用于使用现场可编程门阵列进行基于同态加密的噪声消除和对比度增强。它们需要对加密数据进行除法和最小/最大函数，并且这些操作正在以数字方式实现。当数据分析人员将同态加密驱动的分析结果传递给用户时，会出现另一个问题。它们只能引用与图像部分相关的特定数据点，而不能引用加密后的原始图像本身。这对用户来说可能是有问题的，因为他们可能不理解数据分析师指的是图像的哪一部分。因此，另一个目标是为图像阈值技术开发定制硬件加速器，以突出显示感兴趣的区域。它需要对加密数据进行比较操作，并且再次使用数值方法。与软件实现相比，该项目旨在将计算运行时开销减少一个数量级到两个数量级。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Homomorphic encryption is an important cryptographic technique that allows direct computation on encrypted data without decryption. The result of the computation is entirely encrypted, and only the data owner can decrypt it using a private key. As a result, data analysts such as cloud-service providers cannot view any private or sensitive information from the data. Promising applications of homomorphic encryption include financial and medical data analytics and privacy-preserving machine learning, and homomorphic encryption can be also used for more diverse applications such as genomics, national security/critical infrastructures, and elections. Several emerging applications makes homomorphic encryption increasingly essential. However, homomorphic encryption suffers from slow processing speed, which renders it impractical for many critical applications. In addition, it only supports addition and/or multiplication for encrypted data, which limits the scope of its applications. This project will address these issues using custom hardware accelerators and a numerical approach that approximates several arithmetic and logical operations using addition and multiplication. This project will broaden the scope of practical homomorphic-encryption-based applications and provide opportunities for students to gain experience in a variety of areas such as digital system design, software programming, and cryptography.This project will focus on homomorphic-encryption-based image-processing applications. In typical homomorphic encryption-based systems, a user encrypts private data on a local device and sends them to a cloud server where a homomorphic encryption-based application is performed. However, the inclusion of noise or brightness distortion in the original images can negatively affect the results of the application. Therefore, one of the goals of this project is to develop custom hardware accelerators for homomorphic-encryption-based noise cancelling and contrast enhancement using field-programmable gate arrays. They require division and min/max functions for encrypted data, and these operations are being numerically implemented. Another problem arises when data analysts convey homomorphic-encryption-driven analysis results to a user. They can only refer to specific data points associated with a section of the image, but not the original image itself as it is encrypted. This can be problematic on the user's side as they may not understand which part of the image the data analyst is referring to. Thus, another goal is to develop a custom hardware accelerator for an image-thresholding technique for highlighting regions of interest. It requires comparison operations for encrypted data, and the numerical approach is being used again. This project aims at reducing the computational runtime overhead by one order to two orders of magnitude compared to software implementations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.